{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.distributions import Categorical\n",
    "from util import simulate, recur\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Att_Gnn(\n",
       "  (mlp_in): Sequential(\n",
       "    (0): Linear(in_features=1, out_features=32, bias=True)\n",
       "    (1): ReLU()\n",
       "    (2): Linear(in_features=32, out_features=32, bias=True)\n",
       "    (3): ReLU()\n",
       "  )\n",
       "  (att_layer): Att_layer()\n",
       "  (mlp_out): Sequential(\n",
       "    (0): Linear(in_features=64, out_features=32, bias=True)\n",
       "    (1): ReLU()\n",
       "    (2): Linear(in_features=32, out_features=2, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = torch.load(\"./model/gnn-model_rewrite_0.pkl\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "er_sim = []\n",
    "\n",
    "for i in range(1, 50):\n",
    "    G = nx.erdos_renyi_graph(2000, 8e-5 * i)\n",
    "    avg = G.number_of_edges() * 2 / 2000\n",
    "    ground_truth = simulate(0.04, 0.08, G)\n",
    "    recur_data = recur(model, G)\n",
    "    er_sim.append([avg, ground_truth.item(), recur_data.item()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x282881d8910>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "er_sim = np.array(er_sim)\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(er_sim[..., 0], er_sim[..., 1], label=\"ground_truth\")\n",
    "ax.scatter(er_sim[..., 0], er_sim[..., 2], label=\"gnn\")\n",
    "ax.axvline(4, linestyle='dashed')\n",
    "ax.set_xlabel(\"average degree\", fontsize=12)\n",
    "ax.set_ylabel(\"prevalence\", fontsize=12)\n",
    "ax.set_title(\"SIS -- ER\", fontsize=15)\n",
    "ax.legend()\n",
    "# fig.savefig(\"./images/ER.svg\", format=\"svg\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ba_sim = []\n",
    "\n",
    "for i in range(1, 9):\n",
    "    G = nx.barabasi_albert_graph(2000, i)\n",
    "    avg = G.number_of_edges() * 2 / 2000\n",
    "    ground_truth = simulate(0.04, 0.08, G)\n",
    "    recur_data = recur(model, G)\n",
    "    ba_sim.append([avg, ground_truth.item(), recur_data.item()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_sim = []\n",
    "for i in range(1, 9):\n",
    "    cluster_temp = []\n",
    "    for j in range(1, 10):\n",
    "        G = nx.powerlaw_cluster_graph(2000, i, j / 10)\n",
    "        avg = G.number_of_edges() * 2 / 2000\n",
    "        ground_truth = simulate(0.04, 0.08, G)\n",
    "        recur_data = recur(model, G)\n",
    "        clustering = np.mean(list(nx.clustering(G).values()))\n",
    "        cluster_temp.append([clustering, ground_truth.item(), recur_data.item()])\n",
    "    cluster_sim.append(cluster_temp)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x28287fe1e80>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ba_sim = np.array(ba_sim)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(ba_sim[..., 0], ba_sim[..., 1], label=\"ground_truth\")\n",
    "ax.scatter(ba_sim[..., 0], ba_sim[..., 2], label=\"gnn\")\n",
    "ax.axvline(4, linestyle='dashed')\n",
    "ax.set_xlabel(\"average degree\", fontsize=12)\n",
    "ax.set_ylabel(\"prevalence\", fontsize=12)\n",
    "ax.set_title(\"SIS -- BA\", fontsize=15)\n",
    "ax.legend()\n",
    "# fig.savefig(\"./images/BA.svg\", format=\"svg\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2828437da60>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_sim = np.array(cluster_sim)\n",
    "fig, ax = plt.subplots(figsize=(10, 7))\n",
    "i = 1\n",
    "for item in cluster_sim:\n",
    "    avg_label = \"avg_deg=\" + str(i * 2)\n",
    "    i += 1\n",
    "    ax.plot(item[..., 0], item[..., 1], label=avg_label)\n",
    "    ax.scatter(item[..., 0], item[..., 2])\n",
    "    ax.text(item[-1, 0]+0.02, item[-1, 2] + 0.01, avg_label)\n",
    "ax.set_xlabel(\"clustering coefficient\", fontsize=16)\n",
    "ax.set_ylabel(\"prevalence\", fontsize=16)\n",
    "ax.set_title(\"SIS -- powerlaw cluster graph\", fontsize=20)\n",
    "ax.set_xlim((-0.05, 1))\n",
    "ax.set_ylim((0.1, 1))\n",
    "ax.legend()\n",
    "# fig.savefig(\"./images/BA_cluster.svg\", format=\"svg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 ('dynalearn')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "0c61c7e70abdc191c04ccd6f6f9fbe1bf9c4f458551ead8250184168ad6fc034"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
